Subseasonal-to-Seasonal (S2S) forecasting (between two weeks and a season ahead) is a rapidly developing area of forecasting, with the potential to provide valuable information for the development of climate services. Although S2S climate predictions have a comparative lack of skill beyond two-week lead times, over the past decade there has been a substantial research effort to improve prediction skill via novel advanced statistical and Artificial Intelligence/Machine Learning (AI/ML) methods either in terms of post-processing of the dynamical model output or data-driven models based on teleconnections. Additionally, there is a strong interest in understanding predictability on S2S scales using eXplainable Artificial Intelligence (XAI) which could help to improve forecast skill.

This session welcomes all aspects of improving forecasting on S2S scales including advanced statistical and Artificial Intelligence/Machine Learning (AI/ML) based post-processing (bias correction, multi-model ensemble) of the dynamical model output, and ML models based on teleconnections (empirical/data driven). Abstracts that explore XAI for predictability are also encouraged.